----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\eddie\Desktop\Final\DATA\replication\log.log
  log type:  text
 opened on:  30 Jul 2019, 20:18:05

. 
. 
. *     ***************************************************************** *
. *     ***************************************************************** *
. *     File-Name:  do                                                    *
. *     Date:       March 2019                                            *
. *     Author:     Eddie Hearn                                           *
. *     Purpose:    Hearn_JJPS_2019                                       *
. *     Input File: DATA.dta                                              *
. *     Output File: log.log                                              *
. *     Data Output: None                                                 *
. *     Program:      Stata 14                                            *
. *     Machine:    (Lenovo)Office                                        *
. *     ****************************************************************  *
. *     ****************************************************************  *
. 
. 
. use "C:\Users\eddie\Desktop\Final\DATA\Replication\DATA.dta" 

. 
. recode q3a1 q3a2 q3b1 q3b2 q8  (99 = 3)
(q3a1: 308 changes made)
(q3a2: 205 changes made)
(q3b1: 217 changes made)
(q3b2: 260 changes made)
(q8: 198 changes made)

. recode q13 (99=.)
(q13: 160 changes made)

. 
. gen ego1 = q3a1 
(499 missing values generated)

. gen socio2 = q3a2 
(499 missing values generated)

. gen socio1 = q3b1 
(501 missing values generated)

. gen ego2 = q3b2
(501 missing values generated)

. 
. gen EXP_Group = 1 if ego1==1|ego1==2|ego1==3
(499 missing values generated)

. recode EXP_Group (.=0)
(EXP_Group: 499 changes made)

. 
. gen female = q1-1

. gen age = q2

. gen conserv = q10

. gen collgrad = 1 if q11>8
(581 missing values generated)

. recode collgrad (.=0)
(collgrad: 581 changes made)

. 
. gen employ = q12 

. gen unemployed = 1 if employ ==7
(954 missing values generated)

. recode unemployed (.=0)
(unemployed: 954 changes made)

. 
. gen income = q13 
(160 missing values generated)

. 
. gen ego = ego1
(499 missing values generated)

. replace ego= ego2 if ego==.
(499 real changes made)

. 
. gen socio = socio1
(501 missing values generated)

. replace socio= socio2 if socio==.
(501 real changes made)

. 
. gen trade = q8 

. gen protect = 1 if trade==1
(354 missing values generated)

. replace protect=2 if trade==3
(198 real changes made)

. replace protect =3  if trade==2
(156 real changes made)

. 
. gen sociotropic = 3 if socio==1
(508 missing values generated)

. replace sociotropic =2 if socio==3
(422 real changes made)

. replace sociotropic = 1 if socio==2
(86 real changes made)

. 
. gen egotropic = 3 if ego==1
(628 missing values generated)

. replace egotropic =2 if ego==3
(568 real changes made)

. replace egotropic = 1 if ego==2
(60 real changes made)

. 
. 
. 
. ******************************************************************
. *     Randomization Check                                        *
. ******************************************************************
. 
. probit EXP_Group respid 

Iteration 0:   log likelihood = -693.14518  
Iteration 1:   log likelihood = -693.14364  
Iteration 2:   log likelihood = -693.14364  

Probit regression                               Number of obs     =      1,000
                                                LR chi2(1)        =       0.00
                                                Prob > chi2       =     0.9558
Log likelihood = -693.14364                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
   EXP_Group |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      respid |  -7.10e-06    .000128    -0.06   0.956     -.000258    .0002438
       _cons |   .0063397   .0796816     0.08   0.937    -.1498334    .1625129
------------------------------------------------------------------------------

. 
. 
. probit EXP_Group female age conserv collgrad income

Iteration 0:   log likelihood = -582.09124  
Iteration 1:   log likelihood = -579.53991  
Iteration 2:   log likelihood = -579.53986  

Probit regression                               Number of obs     =        840
                                                LR chi2(5)        =       5.10
                                                Prob > chi2       =     0.4035
Log likelihood = -579.53986                     Pseudo R2         =     0.0044

------------------------------------------------------------------------------
   EXP_Group |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |  -.1211167   .0899762    -1.35   0.178    -.2974667    .0552334
         age |  -.0197373   .0142038    -1.39   0.165    -.0475763    .0081018
     conserv |   .0028978   .0234809     0.12   0.902    -.0431239    .0489195
    collgrad |  -.1198926   .0932575    -1.29   0.199     -.302674    .0628889
      income |  -.0073856   .0159897    -0.46   0.644    -.0387248    .0239536
       _cons |   .2260371   .1830512     1.23   0.217    -.1327367    .5848109
------------------------------------------------------------------------------

. 
. 
. 
. 
. ******************************************************************
. *     H1                                                       *
. ******************************************************************
. 
. tab ego1

       ego1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        168       33.53       33.53
          2 |         25        4.99       38.52
          3 |        308       61.48      100.00
------------+-----------------------------------
      Total |        501      100.00

. tab ego2

       ego2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        204       40.88       40.88
          2 |         35        7.01       47.90
          3 |        260       52.10      100.00
------------+-----------------------------------
      Total |        499      100.00

. 
. ssc inst tab_chi 
checking tab_chi consistency and verifying not already installed...
installing into c:\ado\plus\...
installation complete.

. tabchii 168 25 308 \ 204 35 260, a

          observed frequency
          expected frequency
          adjusted residual

-------------------------------------
          |            col           
      row |       1        2        3
----------+--------------------------
        1 |     168       25      308
          | 186.372   30.060  284.568
          |  -2.404   -1.348    2.992
          | 
        2 |     204       35      260
          | 185.628   29.940  283.432
          |   2.404    1.348   -2.992
-------------------------------------

         Pearson chi2(2) =   9.2029   Pr = 0.010
likelihood-ratio chi2(2) =   9.2210   Pr = 0.010

. 
. 
. ******************************************************************
. *     H2                                                         *
. ******************************************************************
. 
. tab socio1

     socio1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        234       46.89       46.89
          2 |         48        9.62       56.51
          3 |        217       43.49      100.00
------------+-----------------------------------
      Total |        499      100.00

. tab socio2

     socio2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        258       51.50       51.50
          2 |         38        7.58       59.08
          3 |        205       40.92      100.00
------------+-----------------------------------
      Total |        501      100.00

. 
. 
. tabchii 234 48 217 \ 258 38 205, a

          observed frequency
          expected frequency
          adjusted residual

-------------------------------------
          |            col           
      row |       1        2        3
----------+--------------------------
        1 |     234       48      217
          | 245.508   42.914  210.578
          |  -1.456    1.147    0.822
          | 
        2 |     258       38      205
          | 246.492   43.086  211.422
          |   1.456   -1.147   -0.822
-------------------------------------

         Pearson chi2(2) =   2.6708   Pr = 0.263
likelihood-ratio chi2(2) =   2.6739   Pr = 0.263

. 
. ******************************************************************
. *     Trade Attitudes   Ordered                                  *
. ******************************************************************
. 
. oprobit protect collgrad income age female conserv unemployed

Iteration 0:   log likelihood = -720.22176  
Iteration 1:   log likelihood = -701.71938  
Iteration 2:   log likelihood =  -701.6634  
Iteration 3:   log likelihood = -701.66339  

Ordered probit regression                       Number of obs     =        840
                                                LR chi2(6)        =      37.12
                                                Prob > chi2       =     0.0000
Log likelihood = -701.66339                     Pseudo R2         =     0.0258

------------------------------------------------------------------------------
     protect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    collgrad |  -.1872378   .0944207    -1.98   0.047    -.3722991   -.0021765
      income |  -.0427993   .0166594    -2.57   0.010    -.0754512   -.0101474
         age |  -.0303731   .0143413    -2.12   0.034    -.0584816   -.0022646
      female |   .2101044   .0902349     2.33   0.020     .0332472    .3869615
     conserv |   .0750406   .0240223     3.12   0.002     .0279577    .1221234
  unemployed |   .2666767   .2078134     1.28   0.199      -.14063    .6739835
-------------+----------------------------------------------------------------
       /cut1 |   .5664879   .1854104                      .2030902    .9298857
       /cut2 |    1.13364   .1878739                      .7654136    1.501866
------------------------------------------------------------------------------

. 
. 
. oprobit protect collgrad income age female conserv unemployed sociotropic 

Iteration 0:   log likelihood = -720.22176  
Iteration 1:   log likelihood =  -680.9468  
Iteration 2:   log likelihood = -680.74708  
Iteration 3:   log likelihood = -680.74705  

Ordered probit regression                       Number of obs     =        840
                                                LR chi2(7)        =      78.95
                                                Prob > chi2       =     0.0000
Log likelihood = -680.74705                     Pseudo R2         =     0.0548

------------------------------------------------------------------------------
     protect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    collgrad |  -.1241282    .096146    -1.29   0.197    -.3125708    .0643145
      income |   -.041515   .0169467    -2.45   0.014    -.0747298   -.0083001
         age |    -.02144   .0146111    -1.47   0.142    -.0500772    .0071971
      female |   .1602275   .0916148     1.75   0.080    -.0193342    .3397891
     conserv |   .0935637   .0245398     3.81   0.000     .0454666    .1416608
  unemployed |   .1955241   .2099068     0.93   0.352    -.2158855    .6069338
 sociotropic |  -.4479737   .0695268    -6.44   0.000    -.5842438   -.3117037
-------------+----------------------------------------------------------------
       /cut1 |  -.3416649   .2330661                      -.798466    .1151362
       /cut2 |   .2461552   .2335849                     -.2116628    .7039732
------------------------------------------------------------------------------

. 
. 
. oprobit protect collgrad income age female conserv unemployed sociotropic egotropic

Iteration 0:   log likelihood = -720.22176  
Iteration 1:   log likelihood =  -677.4044  
Iteration 2:   log likelihood = -677.14477  
Iteration 3:   log likelihood = -677.14472  

Ordered probit regression                       Number of obs     =        840
                                                LR chi2(8)        =      86.15
                                                Prob > chi2       =     0.0000
Log likelihood = -677.14472                     Pseudo R2         =     0.0598

------------------------------------------------------------------------------
     protect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    collgrad |  -.1144482   .0964828    -1.19   0.236     -.303551    .0746546
      income |  -.0343059   .0172009    -1.99   0.046     -.068019   -.0005928
         age |  -.0239275    .014677    -1.63   0.103    -.0526939    .0048389
      female |   .1358121   .0922309     1.47   0.141    -.0449571    .3165813
     conserv |   .0934831   .0246435     3.79   0.000     .0451827    .1417835
  unemployed |   .1673891   .2112286     0.79   0.428    -.2466112    .5813895
 sociotropic |   -.326095   .0828042    -3.94   0.000    -.4883883   -.1638017
   egotropic |  -.2560975   .0954779    -2.68   0.007    -.4432308   -.0689641
-------------+----------------------------------------------------------------
       /cut1 |  -.6358409   .2579007                     -1.141317   -.1303648
       /cut2 |  -.0452058   .2580972                     -.5510671    .4606555
------------------------------------------------------------------------------

. 
. 
. 
. ******************************************************************
. *     Trade Attitudes   Multinomial                              *
. ******************************************************************
. 
. mlogit protect collgrad income age female conserv unemployed

Iteration 0:   log likelihood = -720.22176  
Iteration 1:   log likelihood =  -690.7316  
Iteration 2:   log likelihood = -689.80742  
Iteration 3:   log likelihood = -689.80411  
Iteration 4:   log likelihood = -689.80411  

Multinomial logistic regression                 Number of obs     =        840
                                                LR chi2(12)       =      60.84
                                                Prob > chi2       =     0.0000
Log likelihood = -689.80411                     Pseudo R2         =     0.0422

------------------------------------------------------------------------------
     protect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
    collgrad |  -.6941642   .2189904    -3.17   0.002    -1.123378   -.2649509
      income |  -.1204763    .040035    -3.01   0.003    -.1989435   -.0420092
         age |  -.1084981   .0317587    -3.42   0.001    -.1707439   -.0462523
      female |   .3410768   .2006571     1.70   0.089    -.0522039    .7343574
     conserv |   .0817466   .0542531     1.51   0.132    -.0245875    .1880808
  unemployed |    .627656   .4243469     1.48   0.139    -.2040487    1.459361
       _cons |   -.692497   .4009174    -1.73   0.084    -1.478281    .0932866
-------------+----------------------------------------------------------------
3            |
    collgrad |  -.1844316   .2096866    -0.88   0.379    -.5954098    .2265466
      income |  -.0564213   .0370682    -1.52   0.128    -.1290736     .016231
         age |  -.0284818   .0324484    -0.88   0.380    -.0920795    .0351159
      female |   .3954869   .2029591     1.95   0.051    -.0023056    .7932793
     conserv |   .1569344   .0537079     2.92   0.003     .0516688    .2622001
  unemployed |   .4846992   .4790273     1.01   0.312    -.4541771    1.423576
       _cons |  -2.088244   .4324331    -4.83   0.000    -2.935797   -1.240691
------------------------------------------------------------------------------

. 
. 
. mlogit protect collgrad income age female conserv unemployed sociotropic

Iteration 0:   log likelihood = -720.22176  
Iteration 1:   log likelihood = -668.11881  
Iteration 2:   log likelihood = -666.48282  
Iteration 3:   log likelihood = -666.47699  
Iteration 4:   log likelihood = -666.47699  

Multinomial logistic regression                 Number of obs     =        840
                                                LR chi2(14)       =     107.49
                                                Prob > chi2       =     0.0000
Log likelihood = -666.47699                     Pseudo R2         =     0.0746

------------------------------------------------------------------------------
     protect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
    collgrad |   -.598175   .2231646    -2.68   0.007     -1.03557   -.1607804
      income |   -.118014    .040654    -2.90   0.004    -.1976944   -.0383336
         age |  -.0940769   .0327798    -2.87   0.004    -.1583242   -.0298296
      female |   .2527069   .2049267     1.23   0.218     -.148942    .6543558
     conserv |    .121339   .0556952     2.18   0.029     .0121785    .2304995
  unemployed |    .515148   .4294539     1.20   0.230    -.3265661    1.356862
 sociotropic |  -.8506943   .1529706    -5.56   0.000    -1.150511   -.5508774
       _cons |   1.020468   .5050889     2.02   0.043     .0305118    2.010424
-------------+----------------------------------------------------------------
3            |
    collgrad |  -.0889534   .2133573    -0.42   0.677    -.5071261    .3292193
      income |  -.0543155   .0375784    -1.45   0.148    -.1279677    .0193368
         age |  -.0130081   .0334362    -0.39   0.697    -.0785419    .0525256
      female |   .3023804   .2066678     1.46   0.143     -.102681    .7074418
     conserv |    .194291    .055161     3.52   0.000     .0861774    .3024046
  unemployed |   .3584491   .4847229     0.74   0.460    -.5915902    1.308489
 sociotropic |    -.79663   .1534455    -5.19   0.000    -1.097378   -.4958822
       _cons |   -.482586   .5250951    -0.92   0.358    -1.511754    .5465815
------------------------------------------------------------------------------

. 
. 
. mlogit protect collgrad income age female conserv unemployed sociotropic egotropic

Iteration 0:   log likelihood = -720.22176  
Iteration 1:   log likelihood = -662.98533  
Iteration 2:   log likelihood = -660.94172  
Iteration 3:   log likelihood = -660.93569  
Iteration 4:   log likelihood = -660.93569  

Multinomial logistic regression                 Number of obs     =        840
                                                LR chi2(16)       =     118.57
                                                Prob > chi2       =     0.0000
Log likelihood = -660.93569                     Pseudo R2         =     0.0823

------------------------------------------------------------------------------
     protect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
    collgrad |   -.570076   .2244361    -2.54   0.011    -1.009963   -.1301894
      income |  -.1010172   .0412062    -2.45   0.014    -.1817799   -.0202545
         age |  -.0997335   .0330572    -3.02   0.003    -.1645245   -.0349425
      female |    .199742    .206514     0.97   0.333     -.205018    .6045019
     conserv |   .1228799   .0564578     2.18   0.030     .0122247    .2335351
  unemployed |   .4659112   .4330471     1.08   0.282    -.3828455    1.314668
 sociotropic |  -.5346016   .1826296    -2.93   0.003     -.892549   -.1766541
   egotropic |  -.6733546   .2140998    -3.15   0.002    -1.092983   -.2537267
       _cons |    1.77826   .5645474     3.15   0.002     .6717671    2.884752
-------------+----------------------------------------------------------------
3            |
    collgrad |   -.079264   .2138432    -0.37   0.711     -.498389    .3398609
      income |  -.0457449   .0380531    -1.20   0.229    -.1203277    .0288378
         age |  -.0164907   .0335587    -0.49   0.623    -.0822646    .0492832
      female |   .2672192   .2077451     1.29   0.198    -.1399537    .6743921
     conserv |   .1977706   .0554962     3.56   0.000     .0890001    .3065411
  unemployed |   .3266121   .4866885     0.67   0.502    -.6272798    1.280504
 sociotropic |  -.6242015   .1848746    -3.38   0.001     -.986549   -.2618539
   egotropic |  -.3669863    .213035    -1.72   0.085    -.7845273    .0505546
       _cons |  -.0619674   .5771845    -0.11   0.915    -1.193228    1.069293
------------------------------------------------------------------------------

. 
. 
. 
. 
end of do-file

. exit, clear
